A wide variety of different types of data storage systems are known, including, by way of example, tiered storage systems, cloud storage systems and storage systems of virtual data centers. These and other data storage systems typically comprise one or more sets of storage devices, possibly configured in the form of storage arrays. Such data storage systems may be associated with what are generally referred to herein as “data stores” of an information processing system.
Enterprises generally want to achieve targeted performance levels from their data stores. However, this goal can be difficult to achieve in practice. For example, an enterprise may implement a single data store to store both low-latency data as well as historical data used for analytics. This type of arrangement is problematic in that the single data store cannot be optimized for both types of data.
It is also possible for an enterprise to implement two entirely separate data stores, one for low-latency data and the other for analytic data. This allows each of the data stores to be optimized for its particular type of data. However, the enterprise will generally have to provide a separate data management system for each data store. In addition, problems arise when applications running above the multiple data stores need to have data from the data stores presented to them in a consistent way. Conventional approaches such as trickle loading from the low-latency data store into the analytic data store fail to achieve consistency of the data across both data stores.
Accordingly, conventional practice is deficient in that when an enterprise uses two separate data stores for low-latency and analytic data, data management becomes increasingly complex, resulting in uneven load, query and update performance, possible gaps in data consistency, and other management difficulties.